To improve clarity and aesthetics of the Data Visualisation plot given
For the DataViz assignment, the following data visualisation will be given a makeover to improve its clarity and aesthetics.
The graph is missing a subtitle which makes it difficult to know what message the data visualisation is trying to convey. A subtitle can be used to give a description on what information is to be conveyed by the charts and a clearer title to emphasize on what the plot is about.
The plots for 70 and over, 70 to 74 and 75 and over have redundancy. Charts used should either use only 70 and above or 70 to 74 and 75 and over as 70 and over will include the age groups 70 to 74 and 75 and above. From the data visualisation, as there is an increasing trend in labour force participation rate from age 70 to 74, removing 70 and above and taking 70 to 74 and 75 and above would be a better option.
The plots have been arranged to look like there is an increasing trend in the labour force participation rate. However, there is no link between each age groups and the plot is giving the wrong perception of this by plotting it from smallest to largest participation rate. It would be better to arrange the plots according to age group for viewers to easily find the age group of interest.
Separate line charts make it difficult to see any comparison or differences between each line trend. Placing all the line trends in one chart but using outstanding colours to highlight those of interest would be better.
The age group labels at the top x-axis for 75 & Over and 70 to Over have been cut, making it difficult to read. It would be better to rotate the labels vertically.
The bottom X-axis of the plot only shows Year 2015 although the individual age group plots show a line trend. The x-axis does not properly show the range of years used.
The label for the Y-axis uses the short-form of Labour Force Participation Rate which is not intuitive for viewers to know what it indicates and the unit of measurement, in this case percentage, is missing. A better y-axis label could be ‘Labour Force Participation Rate (%)’.
The colours used in the plots do not help to identify any information for viewers and seems to be only used for aesthetic purposes. As the line trend throughout the years for each age group is of interest, a proper line plot should be used instead of an area chart to emphasize on the trend.
The improved visualisation will address the cramp line plots for each age group and to allow viewers to easily compare the trends in labour force participation rate across age groups. It will also show the changes in the labour force participation rate between 2010 and 2021. The colours of the line trends that are of interest will be a darker, more contrasting colour while the others will be changed to a lighter shade of colours. The data shown will also not include ‘70 and above’ age group as the ‘70 to 74’ and ‘75 and above’ age groups will be used instead. A more detailed title and subtitle will also be added to the plots. The below alternative Data Visualisation addresses the issues that were brought up above and highlight some observation discovered in the data.
Link to Tableau Public: Final Data Visualisation
Based on the line trend for the different age groups from 2010 to 2021, there has been a steady upward trend in the participation rate for people who are 55 and above while the younger age group has a relatively constant participation rate. The largest increase in participation rate percentage points between 2010 and 2021 is from the age group 65 to 69. This group of people are at the retirement and re-employment age. The increase could be due to Singapore’s government policies to increase retirement and re-employment age to support older people who still wish to continue working. There is also a downward trend seen for the participation rate in age group 20 to 24 which are the group of people that are either have just graduated from Polytechnic or are studying in University. Looking at the line trend for all plots, there is a shift in an older labour force as the participation rate for people below 55 is has a smaller increase in percentage points as compared to those above 55.
Before using the Tableau Prep Builder to clean and prepare the data, row numbers have to be added to the excel sheet to allow for easier extraction of the rows for the different Sex in the dataset.
The data preparation flow to be done is as shown below.
To add steps to the flow, click the + sign next to the icons as shown below.
Step 1: Choose fields required
Step 2: Filter out rows
Step 3: Create Columns to Identify rows for Total, Males and Females
Step 4: Using Pivot Table
Step 5: Extract the final fields required
3. Keep the fields Year, Rate, Age Group and Difference. Select the fields as shown in the blue box. Right click in one of the selected field and select Keep Only
Step 6: Output the file
Below explains how to build the plots in Tableau.
Step 1: Pull in the fields for Columns and Rows
Step 2: Edit Y-Axis Label
Step 3: Add Annotations to Plot
Step 4: Exclude ‘All’ from Plot
Step 5: Add Titles and Subtitles to Plot
Step 1: Create Calculated Field to Extract only 2010 and 2021 Rate Values
Step 2: Pull in the fields for Columns and Rows
Step 3: Combine the two plots into one
Step 4: Rotate the X-Axis Labels
Step 5: Add Titles and Subtitles to Plot